Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky–Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

To improve the overall mechanization level of rapeseed production in China, especially in some hilly regions where ground machinery cannot enter the fields or can only enter with very low economic benefits, a special aerial seeding system for rapeseed based on a miniature air-assisted centralized metering device was designed and tested in this study. Unlike existing commercial aerial seeding systems, the proposed seed meter was a miniaturized version derived from the traditional air-feeding seed meter on ground planters. The new version contained a redesigned seed feeding component to overcome problems of serious air backflow to the seed box and difficult seed feeding after miniaturization. Three groups of experiments were designed and conducted to optimize the parameters of the seed meter and test its performance. Results from the orthogonal experiment showed that the seed feeding component performed best when the seed layer thickness was 45 mm, the rotational speed of the gear disc was 45 r/min, and the airflow pressure was 2450 Pa. Results from the static workbench test showed that the designed seeding system had a maximum average total sowing efficiency of 537.17 g/min, with the maximum values of the stability variation coefficient of total seeding rate (seven ports) and the consistency variation coefficient between each port was 2.37% and 4.89%, respectively. Field tests further proved that the designed aerial seeding system could work stably, uniformly, and efficiently, so that the agronomic requirements of rape crop planting could be well met.

Design and experiment of multi-row pneumatic precision metering device for rapeseed

Existing development for cyclone separation cleaning components of the rapeseed combine harvester, which employs the suspending airflow to separate the rapeseeds from the materials other than grain (MOG), has the challenge to figure out the optimal working parameters, highlighting a need for exploration of the invisible airflow based on Computational Fluid Dynamics (CFD). The airflow status was mainly affected by the air velocities of the inlet, and the outlet for the MOG. The single factor and response surface experiments were carried out. It could be found that the inlet and MOG outlet velocities affected the air velocities through the change in the air quantity. Furthermore, the mathematical model of the relationship between the air velocities inside the cyclone and the working parameters was built, and the optimal combination of working parameters was obtained by multi-objective optimization. The inlet and outlet velocities of the optimal combination were 4.25 m/s and 29.87 m/s, respectively. Under this condition, the cleaning ratio and loss ratio of the cleaning device was 94.62% and 5.39%, respectively, as validated by the field experiment. The findings provide references for the improvement of cleaning systems for rapeseed combine harvesters. Keywords: rapeseed, combine harvester, cyclone separation cleaning, optimal working parameters, CFD DOI: 10.25165/j.ijabe.20231601.7253 Citation: Wan X Y, Yuan J C, Yang J, Liao Y T, Liao Q X. Effects of working parameters on the performance of the cyclone separator for rapeseed combine harvester based on CFD. Int J Agric & Biol Eng, 2023; 16(1): 128–135.

Layered fertilization parameters affects crop root system configuration and growth distribution, which in turn affects soil pore properties and aggregate structure. Therefore, understanding the spatial distribution of the root system and soil pore space is helpful in choosing a reasonable fertilization ratio in production practice, which ensures high and stable crop yields and at the same time improves fertilizer utilization and soil health. Albeit the impact of layered fertilization ratio at varied rates on soil pore characteristics in the soil profile at a microscale level remains limited. This study quantifies the impacts of layered fertilization ratio under on rapeseed root distribution and soil pore characteristics using the root analysis system and X-ray computed tomography (XCT). A new fertilization pattern that shallow-deep layer synchronized mechanical sowing was using, rotary tillage mixed fertilization in the shallow layer and 10 cm side-deep band fertilization in the deeper layer. It set five ratios of fertilizer amounts between shallow and deep layers as 0:4 (FD), 1:3 (FL), 2:2 (FM), 3:1 (FH), and 4:0 (F0), with non-fertilization (CK) as the control treatment. The results indicated that layered fertilization effectively improved the rape root conformation and spatial distribution, significantly increased total soil porosity and moisture content, and reduced soil bulk density and resistance (P < 0.05). Additionally, root configuration is closely related to soil pore structure and crop yield. Notably, compared with other treatments, the macroscopic porosity, equivalent pore diameter, hydraulic radius and roundness of FM treatment are significantly improved, and it effectively improves the yield and quality of rapeseed. Correlation analysis showed that the root system configuration parameters were negatively correlated with soil bulk density and resistance but positively correlated with soil moisture content, and macropore morphology parameters and rapeseed yield. Our study demonstrate that layered fertilization can improved rape growth and soil macropore porosity, but its effect depends on good tillage macropore characteristics and distribution created by reasonable fertilization ratio, which provided a theoretical basis for high-yield, efficient, green, and sustainable mechanized direct-sowing production of rapeseed.